Cumulative Plot Sales

Column

Plots Sold Across the World

Row

Plot Locations

Plots Sold in the US

World Plots

Column

Cumulative Plot Sales

country sold
United States 3878
United Kingdom 454
France 271
Italy 192
Australia 181
Switzerland 179
Japan 177
United Arab Emirates 155
Spain 124
Canada 107
Singapore 105
China 101
Germany 96
Netherlands 71
Egypt 62
Hong Kong 61
Mexico 59
India 53
Austria 51
Portugal 49
Russia 47
Brazil 44
Monaco 39
Turkey 39
Peru 35
Greece 34
Poland 33
Uzbekistan 31
Thailand 30
Vatican City 30
Cambodia 28
Israel 28
South Korea 26
Belgium 19
Saudi Arabia 19
Indonesia 18
Panama 15
New Zealand 14
Qatar 13
South Africa 12
Ukraine 12
coordinates 11
Czech Republic 10
Colombia 8
NA 7
Iraq 6
Ireland 6
Finland 5
Jamaica 5
Jordan 5
Zimbabwe 5
Argentina 4
Denmark 4
Georgia 4
Malaysia 4
Philippines 4
Romania 4
Venezuela 4
Bahamas 3
Cuba 3
Iceland 3
Kazakhstan 3
Saint Barthelemy 3
Saint Lucia 3
Taiwan 3
Armenia 2
Azerbaijan 2
Croatia 2
French Polynesia 2
Ghana 2
Guatemala 2
Nepal 2
Ontario 2
Senegal 2
Bolivia 1
Bulgaria 1
Chad 1
Chile 1
Costa Rica 1
Cyprus 1
Dominican Republic 1
Estonia 1
Ethiopia 1
Hungary 1
Jerusalem District 1
Luxembourg 1
Malta 1
Mongolia 1
Montenegro 1
North Korea 1
Norway 1
Palestinian Territories 1
Serbia 1
Sri Lanka 1
Sweden 1
US Virgin Islands 1
Vietnam 1

Column

Day

Week

Month

Year

Total

US Plots

Column

Cumulative US Plot Sales

state sold
California 738
Texas 735
New York 670
Florida 379
Nevada 310
Illinois 108
District of Columbia 100
Massachusetts 71
Tennessee 68
Louisiana 56
Pennsylvania 53
Georgia 47
Washington 44
Minnesota 36
Hawaii 35
Indiana 34
North Carolina 33
Ohio 33
Kentucky 32
Missouri 30
New Jersey 30
Michigan 29
Arizona 26
Colorado 23
Maryland 20
Virginia 17
Wisconsin 17
Alabama 16
Wyoming 14
Oklahoma 13
Arkansas 12
Oregon 12
Utah 11
Connecticut 4
South Carolina 4
Maine 3
South Dakota 3
Mississippi 2
New Mexico 2
Delaware 1
Iowa 1
Kansas 1
Nebraska 1
New Hampshire 1
New York L2GX5 1
North Dakota 1
Ontario 1

Column

Day

Week

Month

Year

Total

---
title: "SuperWorld Plot Sales"
output: 
  flexdashboard::flex_dashboard:
    social: menu
    source_code: embed
    theme: yeti
---

Cumulative Plot Sales
=====================================

Inputs {.sidebar}
-------------------------------------

```{r setup, include=FALSE, warning=FALSE, message=FALSE}
library(leaflet)
library(leaflet.extras)
library(sf)
library(tidyverse)
library(rnaturalearth)
library(rnaturalearthdata)
library(plotly)
library(usmap)
library(lubridate)

plots_sold = read_csv("C:/Users/rebec/SuperWorld_Plot_Recommendation/data/plots_sold.csv")[-1]
plots_sold$code = toupper(plots_sold$code)

us_plots = plots_sold[which(plots_sold$code == "US"),]
us_address = us_plots$address

state = c()
for (i in 1:length(us_address)){
  add = tail(unlist(str_split(us_address[i], pattern = ", ")), 2)[1]
  add = gsub(' [[:digit:]]+', '', add)
  state = c(state, add)
}

us_plots = cbind(us_plots, state) 

state_data = data.frame(state) %>%
  group_by(state) %>%
  summarise(sold = n())

```

*Total Plot Sales:*

```{r}
nrow(plots_sold)
```


*Top 10 Countries:* ```{r} plots_sold %>% group_by(country) %>% summarise(`plots sold` = n()) %>% arrange(-`plots sold`) %>% head(10) %>% knitr::kable() ```
*Top 10 US States:* ```{r} state_data %>% summarise(state, `plots sold` = sold) %>% arrange(-`plots sold`) %>% head(10) %>% knitr::kable() ``` Column {data-width=800} ------------------------------------- ### Plots Sold Across the World ```{r warning=FALSE, message=FALSE} world = ne_countries(scale = "medium", returnclass = "sf") df = st_sf(merge(plots_sold, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) df_plot = df %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), 0, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradient(trans = "log") + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) # df2 = df %>% # group_by(country, code) %>% # summarise(sold = n()) %>% # mutate(sold = ifelse(is.na(country), 0, sold)) # plot(df2["sold"], logz = TRUE, main = NULL, key.pos = 4) ggplotly(df_plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` Row ------------------------------------- ### Plot Locations ```{r} leaflet(plots_sold) %>% addTiles() %>% addCircles(lng = ~lon, lat = ~lat) %>% setView(lat = 37.0902, lng = -95.7129, zoom = 4) ``` ### Plots Sold in the US ```{r} us = plot_usmap(data = state_data, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_continuous(name = "Plots Sold") ggplotly(us) ``` World Plots ===================================== Column {data-width=200} ------------------------------------- ### Cumulative Plot Sales ```{r} plots_sold %>% group_by(country) %>% summarize(sold = n()) %>% arrange(-sold) %>% knitr::kable() ``` Column {.tabset} ------------------------------------- ### Day ```{r} plots_today = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 1) df_today = st_sf(merge(plots_today, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_today %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), 0, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE)) ``` ### Week ```{r} plots_week = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 7) df_week = st_sf(merge(plots_week, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_week %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` ### Month ```{r} plots_month = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 30) df_month = st_sf(merge(plots_month, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_month %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` ### Year ```{r} plots_year = plots_sold %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 365) df_year = st_sf(merge(plots_year, world, by.x = "code", by.y = "iso_a2", all.x = FALSE, all.y = TRUE, returnclass = "sf")) plot = df_year %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(5, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` ### Total ```{r} plot = df %>% group_by(country, code) %>% summarise(sold = n()) %>% mutate(sold = ifelse(is.na(country), -Inf, sold)) %>% ggplot() + geom_sf(aes(fill = sold))+ scale_fill_gradientn(trans = "log", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) + geom_sf_text(aes(label = code), size = 1) + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), legend.title = element_text("Plots Sold")) + labs(caption = "Sold values are in log scale") + guides(fill = guide_colourbar(barwidth = 0.5, barheight = 10)) ggplotly(plot) %>% layout(annotations = list(x = 1, y = -0.1, text = "Sold values are in log scale", showarrow = F, xref='paper', yref='paper', xanchor='right', yanchor='auto', xshift=-10, yshift=80, font=list(size=15)), xaxis = list(autorange = TRUE), yaxis = list(autorange = TRUE) ) ``` US Plots ===================================== Column {data-width=200} ------------------------------------- ### Cumulative US Plot Sales ```{r} us_plots %>% group_by(state) %>% summarize(sold = n()) %>% arrange(-sold) %>% knitr::kable() ``` Column {.tabset} ------------------------------------- ### Day ```{r} us_today = us_plots %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 1) %>% group_by(state) %>% summarise(sold = n()) us_today = plot_usmap(data = us_today, values = "sold", regions = "states") + theme(legend.position = "right") + # scale_fill_continuous(name = "Plots Sold") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_today) ``` ### Week ```{r} us_week = us_plots %>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 7) %>% group_by(state) %>% summarise(sold = n()) us_week = plot_usmap(data = us_week, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_week) ``` ### Month ```{r} us_month = us_plots%>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 30) %>% group_by(state) %>% summarise(sold = n()) us_month = plot_usmap(data = us_month, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_month) ``` ### Year ```{r} us_year = us_plots%>% mutate(days = interval(date, today()) %/% days(1)) %>% filter(days < 365) %>% group_by(state) %>% summarise(sold = n()) us_year = plot_usmap(data = us_year, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us_year) ``` ### Total ```{r} us = plot_usmap(data = state_data, values = "sold", regions = "states") + theme(legend.position = "right") + scale_fill_gradientn(name = "Plots Sold", colors = colorspace::heat_hcl(12, h = c(-60, -150), l = c(75, 40), c = c(40, 80), power = 100)) ggplotly(us) ```